Transtibial amputees typically employ passive dynamic-elastic-response foot/ankle prostheses, which are essentially relatively stiff leaf springs, typically configured with a nominal angle of 90 degrees between the foot and shank. The behavior of the ankle joint in a healthy limb is considerably more varied than the spring-like behavior provided by passive ankle prostheses. In particular, the healthy ankle exhibits a variety of behaviors, including passive behaviors, such as stiffness and/or damping, and active behaviors, such as powered push-off or controlled motion generation. Additionally, these behaviors vary considerably depending on a given activity or type of terrain. For example, when the ankle exhibits a spring-like behavior, the stiffness and equilibrium point of the stiffness will generally vary greatly, depending on the activity and terrain. As such, a passive ankle prosthesis is only able to provide a small subset of the full range of healthy ankle behavior.
Recent advances in battery, microprocessor and motor technologies have made possible the emergence of powered prostheses. An appropriately-designed powered ankle prosthesis is able to emulate the full range of biomechanical behaviors provided by the healthy joint. In order to do so, a prosthesis requires a sensing, actuation, and a transmission system capable of emulating the range of healthy joint impedances observed during locomotion, as well as a control system that recognizes the activity in which the user is currently engaged and provides the appropriate joint behavior accordingly.
Some control strategies for an ankle prosthesis have been described in the engineering literature. Holgate et al. [1] describes a “tibia based controller theory” which essentially seeks to find a continuous relationship between shank angle and ankle angle and a scaling factor based on speed, and a “dynamic pace control”, which continuously modulates the ankle period and amplitude based on walking speed. A powered ankle control strategy presented by Au et al. [2] describes first a neural network model and secondly a neuromuscular model, both of which rely on electromyogram (EMG) signal inputs from the amputee's residual limb to position the ankle angle. In another control strategy the phases of gait are decomposed into four parts, and a finite state controller utilizes combinations of linear springs and nonlinear springs, coupled with torque and position sources, respectively, for each portion [3]. An extension of this method has one finite state controller for level ground walking and one for stair climbing, and uses EMG signals from the user to switch between controllers [4]. Finally, [5] presents an approach based on a two state model, one for swing and the other for stance. The swing phase employs position control and the stance phase incorporates a Hill-type muscle model which reacts with a force in proportion to position and speed.